Stock Market Performance By Presidential Administration¶

Introduction¶

Some Americans, including Republican presidential candidates, claim US voters should vote for Republican presidential candidates because the stock market goes up more under Republican presidents.

This isn't a good argument: first, because the president doesn't directly control the values of stocks; second, because, in recent history, the stock market has grown much more under Democratic administrations than republican ones.

Let's take a quick, informal look at the data.

Data Source¶

I'm going to use Yahoo Finance as the data source. It's a free service and it allows you to download stock market quotations by ticker for a specified period of time. I downloaded the data from the Dow Jones Index (^DJI) for the period from 1989-01-20 to 2021-01-15 (the latest date available when I pulled the data) and saved that as the file 'DJIbyPresident.csv'.

I've chosen to use the DJI as a proxy for the stock market as a whole because the 30 companies that are memebers of the Dow represent a huge share of the overall stock market (by market cap). We could have chosen a broader index like the S&P500, or the NASDAQ, but over the 30 year timescale that we've chosen to look at, the differences between the performance of these indices are minor compared to the overall trend over time.

The adjusted close is the price after adjusting for any divided payouts or stock splits.

Now we can just select the days of presidential inaugurations...

Now we will create a second dataframe with the information about the presidential administrations.

What we want to do now is calculate the percentage change between the start and ending dates of each administration. Because each administration is the same length of time, four years, we can simply calculate the percentage change as:

$$ change = \frac{(end - start)}{start} \cdot 100 $$

If we wanted to bundle administrations together, then we'd have to annualize the return using CAGR, because we'd be comparing administrations that were different lengths of time.

Let's inspect that visually...

Let's summarize further with a simple box plot.

If we wanted to, we could set up a statistical test to evaluate how likely it would be to observe data like these given the hypothesis that the DJI goes up more under Republican presidents than Democratic ones.

However, this is unnecessary. We can already see that the last 30 years offers no support for this hypothesis at all.

Conclusion¶

So, can we conclude from this that Democratic presidents are better than Republican ones for DJI investors?

No.

First, as we said above, the president doesn't directly control stock market prices.

Second, there are a huge number of factors that drive stock prices, and while the president may exert some influence on some of those factors, an analysis in terms of the party of the president is going to be way too simple to explain much of the differences we observe in the stock data.

Third, there have only been eight administrations in the last 32 years, so there isn't a huge amount of data available here. We could try to get more data by extending the time period of the study, but then we run into the problem that parties change their platforms over time. Political party affiliation is a proxy for a set of policy positions, but the further out in time we extend our study, the less similar members of the same party's policy positions will be. Further, the factors that drive the stock market itself changes, and the further back in history we go, the less likely the those factors that explained stock price changes in the past will explain stock prices in the present and near future.

Introduction¶

In the last post, we defined our technical requirements and (after some exploration) settled on Nikola as the platform for the blog. In this installment, we'll create an environment, install Nikola, get everything set up, build our first post, and then push the whole thing to GitHub pages.

Step 1 -- Creating the Repository¶

I'm assuming that you already have a good version of python 3 and git installed and that you already have an account on github

1. Log in to github and create a new repository named <yourUsername>.github.io where <yourUsername> is your username.
   • Choose a .gitignore for python, and a license if you want.
2. Now you'll need to clone this new repository to the local machine where you'll be working.
   • On a mac, I did this with git clone git@github.com:shanewilkins/shanewilkins.github.io.git from the directory above where I wanted the new project folder shanewilkins.io.git to live.

Step 2 -- Creating a Python Environment¶

1. Navigate to the new project folder cd <yourUsername>.github.io
2. Create a virtual environment for our project with virtualenv venv which should create a new subfolder called venv in the project folder.
3. Activate the virtual environment with source venv/bin/activate. You should see (venv) to the left of your command prompt.
   • You can do python --version or which python to make sure that the active version of python is the right version from the virtual environment.
4. Update the installed versions of the "seed" packages python -m pip install -U pip setuptools wheel

Step 3 -- Installing Nikola and Initializing a Site¶

1. Now we install Nikola with pip install "nikola[extras]". This will take a couple of minutes. Since Nikola supports Jupyter out of the box, Nikola has to depend on all of Jupyter's numerous dependencies too.
2. Once we have Nikola installed, we are ready to start the blog! All we have to do is nikola init and Nikola will start an interactive prompt to guide us through some setup questions. (Don't worry about choosing the wrong thing--you can change all of these settings later in conf.py.)

3. Open conf.py and make sure of three things:

   • SITE_URL = "https://<yourUsername>.github.io",
   • GITHUB_DEPLOY_BRANCH = 'main' (not master), and
   • POSTS can find .ipynb with

POSTS = (
    ...
    ("posts/*.ipynb", "posts", "post.tmpl")
)

Step 4 -- Writing the First Post¶

1. Now to create your first post (which will be a Jupyter notebook) do nikola new_post -f ipynb and tell the prompt what you'd like the name of this post to be. (Note. all the new_post command is really doing is populating notebook metadata, which you can edit later from inside Jupyter with Edit>Edit Notebook Metadata
2. Now fire up the notebook with jupyter notebook, and you should find your new post in the posts directory.
3. Write your post! :)
4. Save the post and close Jupyter.

Step 5 -- Publish to Github Pages¶

1. From the commandline in your main project folder now you can simply do nikola github_deploy and Nikola will automagically build the site, commit changes, and push to the remote repository.
2. Go get a cup of coffee.
3. Now navigate in your browser of choice to https://<yourUsername>.github.io and, if all has gone well, you should see your brand new blog appear, courtesy of github pages.

Test Post¶

Basic Markdown Functions¶

Text¶

italics

bold

~strikethrough~

links

blockquotes

Lists:¶

Ordered¶

1. One
2. Two

Unordered¶

• Cat
• Bat

Checkboxes¶

• [x] Checkboxes
• [ ] Theorem environments

(For some reason, as of the time of writing the checkboxes also have bullets beside them, which is not desired.)

Unicode text¶

μῆνιν ἄειδε θεὰ Πηληϊάδεω Ἀχιλῆος

οὐλομένην, ἣ μυρί᾽ Ἀχαιοῖς ἄλγε᾽ ἔθηκε,

πολλὰς δ᾽ ἰφθίμους ψυχὰς Ἄϊδι προΐαψεν

ἡρώων, αὐτοὺς δὲ ἑλώρια τεῦχε κύνεσσιν

5οἰωνοῖσί τε πᾶσι, Διὸς δ᾽ ἐτελείετο βουλή,

ἐξ οὗ δὴ τὰ πρῶτα διαστήτην ἐρίσαντε

Ἀτρεΐδης τε ἄναξ ἀνδρῶν καὶ δῖος Ἀχιλλεύς.

Images from the web¶

Mathematics¶

The flavor of Markdown I'm using supports $\LaTeX$ typesetting of mathematics via MathJax.

For instance, here are Maxwell's equations:

\begin{align} \nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\ \nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\ \nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\ \nabla \cdot \vec{\mathbf{B}} & = 0 \end{align}

It doesn't seem to support theorem environments, alas.

Python code¶

Data frames¶

Charts and Graphs¶

Line Charts¶

I'm using Plot.ly for my graphing, because it offers an intuitive, easy-to-use API and because it supports interactive visualizations.

Scatterplots¶

Area charts¶

Another example visualizing topographical data with a color scheme.

Geodata visualizations¶

Another cool chloropleth example from the docs that utilizes GeoJSON to visualize the unemployment rate in the US by county.